Lossless data compression with low complexity

ABSTRACT

An adaptive predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding to thereby compress a digital signal which includes the samples. The adaptive predictor adapts to residuals between actual and predicted samples at a particular rate. The rate of adaptation is itself adapted periodically to ensure optimum performance of the compression. A number of samples are repeatedly compressed using different adaptation rates, and the adaptation rate which produces the best compression results is used. The adaptation rate can be an exponential power of 2 such that incrementing the adaptation rate effectively doubles the rate at which the predictor is adapted and decrementing the adaptation rate effectively halves the rate at which the predictor is adapted. Accordingly, fewer trials are needed to find a relatively optimal adaptation rate for the predictor. A code length used in Golomb-Rice, which is typically referred to as the parameter k, is adapted for each sample in a predictable and repeatable manner to further reduce the size of a Golomb-Rice encoding for each sample. An infinite incident response filter of processed residuals automatically reduces influences of previously processed residuals upon such adaptation as additional samples are processed. The efficiency of Golomb-Rice encoding is improved by limiting the predicted samples to an efficient range.

FIELD OF THE INVENTION

The present invention relates to data compression and, in particular, toa particularly efficient and computationally simple lossless datacompression mechanism which achieves particularly good compression ratesfor digitized audio signals.

BACKGROUND OF THE INVENTION

Data compression has held a prominent place in computer science for manyyears as demand for additional data capacity of various systems increasewhile storage capacity and bandwidth of such systems are limited. Datacompression generally falls into one of two categories: lossy andlossless. In lossy data compression, particularly high rates of datacompression are achieved at the expense of distortion of the data. Suchis sometimes acceptable for image, video and audio data since smalldistortions may be only slightly perceptible by a human viewer orlistener of the subsequently decompressed data. However, in a number ofapplications, such distortion of the compressed data is unacceptable.

Lossless data compression reduces the size of the overall representationof data without any distortion of the data at all, i.e., without anyloss of data. Lossless data compression is used primarily forcompression of text, computer programs, and databases where faithfulreproduction of the compressed data is essential. However, asdistribution of high-quality digitized audio signals through computernetworks becomes more prevalent, lossless compression of digitized audiosignals with good data compression rates grows in importance.

In general, data is compressed by recognizing patterns in the data andrepresenting such patterns in compact forms in place of the data havingthe recognized patterns. The degree of compression realized by aparticular lossless compression mechanism depends in large part upon thecorrelation between patterns recognized by the mechanism and patterns inthe data to be compressed. Since the majority of currently availablelossless data compression mechanisms are designed for compressingtextual data, such mechanisms achieve relatively good results whencompressing data having patterns typically found in textual data.However, such mechanisms generally fail to achieve results as good whencompressing non-textual data, e.g., data representing digitized audiosignals.

In addition, the rate of data compression of lossless data compressiontechniques is generally inversely related to the complexity of suchtechniques. In real time delivery of compressed data through a deliverymedium having limited bandwidth, a relatively high rate of compressionof the compressed data is essential and a minimum acceptable rate ofdata compression is limited by the limited bandwidth of the deliverymedium. At the same time, the complexity of the manner in which the datais compressed must generally be minimized such that the deliveredcompressed data can be decompressed without exceeding the processingbandwidth of a receiving system. Improving the rate of compressionrealized by a lossless data compression mechanism without simultaneouslyincreasing the complexity of the lossless data compression isparticularly difficult.

What is needed is a system for performing lossless data compression insuch a way that particularly good data compression rates are realized,particularly when compressing data representing digitized audio signals,while simultaneously maintain a particularly low level of complexity ofsuch lossless data compression.

SUMMARY OF THE INVENTION

In accordance with the present invention, an adaptive linear predictoris used to predict samples, and residuals from such predictions areencoded using Golomb-Rice encoding. Golomb-Rice encoding is particularlyefficient and simply implemented. However, unless residuals areminimized and have a generally exponential distribution, Golomb-Riceencoding has marginal performance, at best, in terms of compressionrates. Linear prediction of samples of a signal which representsdigitized sound tends to produce relatively low residuals and thoseresiduals tend to be distributed exponentially. Accordingly, linearprediction combined with Golomb-Rice encoding produces particularly goodcompression rates with very efficient and simple implementation.

To further improve the compression rates achievable using Golomb-Riceencoding, a code length used in Golomb-Rice, which is typically referredto as the parameter k, is adapted for each sample in a predictable andrepeatable manner to further reduce the size of a Golomb-Rice encodingfor each sample. Some conventional adaptations of a code length use astraight average of a number of recent residuals to adapt the codelength. Such systems suffer from the disadvantage that remote residuals,i.e., residuals of samples processed in the relatively distant past,have as much influence on the adaptation of the code length as do nearresiduals, i.e., residuals of samples processed in the relatively recentpast. Some such systems require periodic resetting of the code lengthadaptation mechanism to eliminate undue influence upon the adaptation ofthe code length of residuals of samples processed to remotely.

In accordance with the present invention, an infinite incident responsefilter of processed residuals automatically reduces influences ofpreviously processed residuals as additional samples are processed. Inaddition, the influence of each residual processed in the adaptation ofthe code length is directly related to the recency of the processing ofthe sample to which the residual corresponds. Furthermore, no resettingof the code length adaptation mechanism is required since influence ofparticularly distant residuals upon the adaptation of the code lengthdiminishes to negligible amounts over time. In addition, the IIR filteris particularly simple to implement. For example, the weights of thepreviously filtered residual and the current residual can be (2^(j)-1)/2^(j) and 1/2^(j), respectively, where j is an integer. Accordingly,the previously filtered residual can be weighted using integerarithmetic rather than floating point arithmetic to further expedite theprocessing of the lossless compressor according to the presentinvention. Specifically, the previously filtered residual is weighted bya bit-shift j places to the left from which the previously filteredresidual is subtracted. The current residual is added to the result andthe sum is bit-shifted j places to the right to effect division.Accordingly, four integer operations perform a single iteration of theIIR filter and, in addition, superior results are achieved.

Further in accordance with the present invention, the efficiency ofGolomb-Rice encoding is improved by limiting the predicted samples to anefficient range. In general, the representation of a sample of a digitalsignal is effectively limited to the particular values that can berepresented by such a representation. In encoding a residual accordingto the present invention, a least significant portion of the residual isrepresented in a fixed-length, binary form and a most significantportion is represented in a variable-length, unary form. The maximum ofthe efficient range of the predicted sample is chosen such that nopossible value of the fixed-length, binary portion of an encodedresidual can be added to the limited predicted sample to produce a valuewhich is beyond the range of valid sample values. Specifically, themaximum of the efficient range is the maximum valid value of a sampleless the maximum positive value of the fixed-length, binary portion ofan encoded residual. Accordingly, bits are not wasted in thevariable-length, unary portion to represent unduly large residuals. Inaddition, the full range of the fixed-length, binary portion isutilized. At the other end of the efficient range, the minimum is chosensuch that no possible value of the fixed-length, binary portion of anencoded residual can be added to the limited predicted sample to producea value beyond the range of valid samples. Specifically, the minimum ofthe efficient range is the minimum valid value plus the minimum negativevalue of the fixed-length, binary portion of an encoded residual.

As described briefly above, the lossless compressor according to thepresent invention includes an adaptive predictor. The adaptive predictoradapts to residuals between actual and predicted samples at a particularrate. Further performance improvements in the lossless compression ofsignals according to the present invention in terms of compression ratesare realized by periodically adapting the adaptation rate. A portion ofthe signal including a number of samples is compressed using aparticular adaptation rate. The adaptation rate is adjusted and theportion is compressed again using the adaptation rate as adjusted. Theresulting compressed portions are compared to determine which adaptationrate produces the better results. The adjusting, compressing, andcomparing process is repeated iteratively until the best adaptation rateis determined.

Adjusting the adaptation rate increments or decrements the adaptationrate. In addition, the adaptation rate specifies an exponent of anamount by which the predictor adapts to residuals such that unitaryadjustments to the adaptation rate change the amount of adaptation ofthe predictor exponentially. In particular, the adaptation rate is anexponent of two (2) such that incrementing the adaptation rateeffectively doubles the rate at which the predictor adapts according toresiduals and decrementing the adaptation rate effectively halves therate at which the predictor adapts according to residuals. In addition,adapting the predictor by integer factors of two (2) allows suchadaptation to be accomplished using bit shifts to further simplify andexpedite compression according to the present invention.

Further in accordance with the present invention, correlation betweencompanion channels of a digital signal are used to improve the accuracyof sample prediction such that residuals, and therefore representationsthereof, are minimized. For example, if a digital signal represents aleft channel and a right channel of a stereo sound, correspondingsamples of the left and right channels have a relatively high degree ofcorrelation. Accordingly, a sample of the right channel is predictedusing, in addition to previously processed samples of the right channel,a current sample of the left channel. Because of the relatively highdegree of correlation, the accuracy with which the current right channelis predict is increased.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a lossless compressor in accordance withthe present invention.

FIG. 2 is a block diagram of a predictor of the lossless compressor ofFIG. 1.

FIG. 3 is a block diagram of a code length adapter of the predictor ofFIG. 2.

FIG. 4 is a number line diagram illustrating the clamping of predictedsamples produced by the predictor of FIG. 2.

FIGS. 5A-C are block diagrams illustrating Golomb-Rice encoding.

FIG. 6 is a logic flow diagram of the adaptation of the adaptation rateof the predictor of FIG. 2.

FIG. 7 is a block diagram of a lossless decompressor in accordance withthe present invention.

FIG. 8 is a block diagram of left and right predictors for losslesscompression in accordance with the present invention.

FIGS. 9A-B are block diagrams of the right and left predictors,respectively, of FIG. 8 in greater detail.

FIG. 10 is a block diagram of left and right predictors for losslessdecompression in accordance with the present invention.

FIG. 11 is a block diagram of a predictor coefficient adapter of thepredictor of FIG. 2.

FIG. 12 is a block diagram of a computer system within which thelossless compressor of FIG. 1 and the lossless decompressor of FIG. 7execute.

DETAILED DESCRIPTION

In accordance with the present invention, the simplicity of Golomb-Riceencoding is combined with a adaptive linear predictor to achieveparticularly good compression rates for lossless compression ofdigitized audio signals while preserving the computational simplicityand commensurate processing speed of Golomb-Rice encoding. Specifically,a lossless compressor 100 (FIG. 1) includes a predictor 102 which uses aleast-mean-square adaptive linear filter to predict a next sample fromprevious samples of a digital signal and further includes a coder 110which encodes a residual, i.e., a difference between the predictedsample and the actual sample, using Golomb-Rice encoding. Golomb-Riceencoding is well-known but is described briefly below for completeness.

Predictor 102 receives a source sample of a digitized signal to becompressed and encoded by lossless compressor 100 and predicts therefroma next sample in a manner described more completely below and forwardsthe predicted next sample to a delay 104. When the next source sample isreceived by lossless compressor 100, a subtracter 112 measures thedifference between the next source sample received by losslesscompressor 100 with the predicted next sample stored in delay 104. Themeasured difference is referred to herein a raw residual. The rawresidual is a measure of error between the predicted next sample and theactual next sample and is therefore used by predictor 102 to adapt thenature of the prediction to more accurately predict future next samples.This adaptation is described more completely below.

In addition, lossless compressor 100 includes a code length adapter 106which uses the raw residual to adapt a code length used by coder 110 ina manner described more completely below to more efficiently encoderesidual samples. The residual samples encoded by coder 110 are not theraw residual samples described above but are instead clamped residualsamples received from subtracter 114. Clamped residual samples furtherimprove the efficiency with which coder 110 encodes residual samples ina manner described more completely below.

It should be noted that efficiency of a Golomb-Rice encoder is maximizedwhen the values being encoded have an exponential distribution.Predictor 102 is designed to produce raw residual samples which have agenerally exponential distribution for source samples of a digitizedaudio signal and is shown in greater detail in FIG. 2. Predictor 102includes a delay buffer 202 which stores a number of most recentlyreceived source samples and a number of corresponding predictorcoefficients. Each of the most recently received source samples areweighted by a respective one of the predictor coefficients andaccumulated to produce a predicted sample. Predictor 102 is therefore alinear filter. In addition, the predictor coefficients are selected soas to form a least mean square linear filter in one embodiment.

Predictor coefficients 204 are adaptive in response to raw residualsamples formed from previously predicted samples. Specifically, apredictor coefficient adapter 206 receives the raw residual sample fromsubtracter 112 (FIG. 1) and weights the raw residual sample with anadaptation rate 208 (FIG. 2) to produced a weighted error. Predictorcoefficient adapter 206 decreases each of predictor coefficients 204 bythe product of the corresponding source sample of delay buffer 202 andthe weighted error to thereby adapt each of predictor coefficients 204as shown in FIG. 11. To improve efficiency of predictor coefficientadapter 206 and to achieve additional advantages described morecompletely below, adaptation rate 208 is an integer power of 2.Accordingly, weighting of the raw residual sample with adaptation rate208 can be implemented as a bit-wise shift of the raw residual sampleor, alternatively, of the product of the raw residual sample and each ofpredictor coefficients 204. Adaptation rate 208 controls howaggressively predictor 102 (FIG. 2) compensates for large raw residualsamples.

While Golomb-Rice encoding is well-known, a brief discussion ofGolomb-Rice encoding facilitates appreciation of various components oflossless compressor 100 (FIG. 1). FIG. 5A shows a data word 502A whichincludes a number of bits, n. For example, data word 502A has 16 bits inone embodiment, i.e., n is 16. Data word 502A includes a sign bit 504which indicates whether the value represented by data word 502A ispositive or negative. Golomb-Rice encoding generally requires that theencoded value is non-negative. Therefore, coder 110 (FIG. 1) (i)retrieves and saves sign bit 504 (FIG. 5A), (ii) changes the sign of thenumerical value represented by the remainder of data word 502A if signbit 504 indicates data word 502A is negative, (iii) shifts data word502A to the left one bit to form data word 502B (FIG. 5B), and (iv)stores sign bit 504 as the least significant bit of data word 502B. Withsign bit 504 moved to the least significant position, data word 502B isnon-negative. In addition, the relative magnitude of data word 502A(FIG. 5A) is preserved in data word 502B (FIG. 5B) such that, if valuesrepresented by data word 502A have a generally two-sided, signed,exponential distribution, values represented by data word 502B (FIG. 5B)also have a generally one-sided, unsigned, exponential distribution. Oneway to conceptualize this conversion is to assume an implicit decimalpoint immediately prior to sign bit 504 as rotated such that a negativeinteger, j, is represented as |j|+1/2.

After decoding as described more completely below, the sign of data word502A (FIG. 5A) is restored by (i) retrieving and saving sign bit 504(FIG. 5B) from data word 502B, (ii) shifting data word 502B to the rightby one bit to form data word 502A (FIG. 5A), (iii) and changing the signof the numerical value represented by data word 502A if sign bit 504(FIG. 5B) of data word 502B indicates a negative value.

In Golomb-Rice encoding, a least significant binary portion 508 (FIG.5C) of data word 502B is represented as a binary number which includes anumber of bits, k where k<n. In other words, least significant portion508 is represented without modification. A most significant unaryportion 506 of data word 502B is represented in unary form. As anexample, consider the Golomb-Rice encoding of the following bit pattern:"0000 0000 0101 1101" in which k is equal to five (5). The five (5)least significant bits of least significant binary portion 508 arerepresented in binary form without modification, i.e., as "1 1101." Theeleven (11) most significant bits of most significant unary portion 506,i.e., "0000 0000 010," have a value of two (2) and are represented inunary form. Specifically, a series of two (2) "1" bits indicates a valueof two (2) and a "0" bit delimits the series of "1" bits.

Therefore, efficiency of Golomb-Rice encoding is maximized when thenumerical value of most significant unary portion 506 is minimized. Inother words, the amount of data required to represent most significantunary portion 506 is generally directly related to the numerical valueof most significant unary portion 506. Two factors thereforeparticularly effect the efficiency of Golomb-Rice encoding. The firstfactor is the distribution of data values encoded using Golomb-Riceencoding. The second factor is the position of the boundary betweenleast significant binary portion 508 and most significant unary portion506.

With respect to the distribution of data values encoded usingGolomb-Rice encoding, reasonably optimal results can be achieved whenthe distribution of data values encoded is generally exponential and thedata values themselves are relatively minimal. In compressing adigitized audio signal, the samples of the digitized audio signal aretypically related to one another as audio signals are typicallycontinuous in frequency, amplitude, and phase. As a result, individualsamples of a digitized audio signal can be relatively accuratelypredicted from a linear filter applied to a number of recently precedingsamples of the digitized audio signal. The accuracy with which suchindividual samples are predicted is enhanced by adapting the linearfilter in accordance with residuals between predicted and actual samplesin a manner described more completely below.

The position of the boundary between least significant binary portion508 and most significant unary portion 506, i.e., the size of leastsignificant binary portion 508, also affects the efficiency ofGolomb-Rice encoding. If the size of least significant binary portion508, i.e., k, is too small, the numerical value of most significantunary portion 506 will frequently be too large and the amount of datarequired to encode most significant unary portion 506 will be too largeas a consequence. Conversely, if the size of least significant binaryportion 508, i.e., k, is too large, then all k bits of least significantbinary portion 508 will be used to encode numerical values which couldhave been encoded using fewer bits.

Code length adapter 106 (FIG. 1) uses the raw residual produced bysubtracter 112 to determine a relatively optimum data length of leastsignificant binary portion 504 such that encoding of the raw residualwould be particularly efficient. Specifically, code length adapter 106(FIG. 1), which is shown in greater detail in FIG. 3, includes a codelength generator 306. Code length generator 306 produces a code lengthfrom a filtered residual received from an infinite impulse response(IIR) filter 304. IIR filter 304, in this illustrative embodiment, is atwo-tap IIR filter in which the magnitude of the previous filteredresidual is weighted and accumulated with a weighted current rawresidual. The magnitude of the previous filtered residual is produced byan absolute value filter 302 and is measured by determining the absolutevalue of the value of the previous filtered residual.

IIR filter 304 has the property that the relative weight of any rawresidual magnitude incorporated in IIR filter 304 is directly related tothe recency of the raw residual. In other words, the most recentlyincorporated raw residual magnitude has the highest weight relative toother raw residual magnitudes incorporated in IIR filter 304, the nextmost recently incorporated raw residual magnitude has the next highestrelative weight, and so on. By comparison, a straight average of anumber of recent raw residuals gives equal weight to each residualregardless of the recency of each of the raw residuals. IIR filter 304has an additional advantage of simple implementation; as new rawresidual magnitudes are incorporated into IIR filter 304, the influenceof earlier raw residual magnitudes upon IIR filter 304 decrease.Accordingly, IIR filter 304 remains effective throughout losslesscompression of an entire digitized audio signal without requiringexplicit clearing of influence of increasingly remote raw residualmagnitudes.

In an embodiment which executes particularly efficiently, the weightattributed to the previous filtered residual magnitude is (2^(j)-1)/2^(j) and the weight attributed to the current raw residualmagnitude is 1/2^(j) where j is an integer. Accordingly, the previousfiltered residual magnitude is weighted by bit-shifting the previousfiltered residual magnitude to the left j positions to thereby multiplythe previous filtered residual magnitude by 2^(j) and the previousfiltered residual magnitude is subtracted from the product to form aproduct of the previous filtered residual magnitude by 2^(j) -1. IIRfilter 304 accumulates the product with current raw residual magnitudeand right-shifts the sum by j positions to effectively divide the sum by2^(j). Thus, IIR filter 304 produces a filtered residual magnitude fromthe previous filtered residual magnitude and the current raw residualmagnitude using only four (4) integer operations. IIR filter 304therefore executes with great efficiency.

Code length generator 306 determines the length of least significantbinary portion 508 (FIG. 5C) from the filtered residual magnitude.Specifically, code length generator 306 (FIG. 3) determines the minimumk such that 2^(k) is greater than or equal to the filtered residualmagnitude. IIR filter 304 serves as a particularly good predictor forthe amount of data required to represent the current raw residual,especially for lossless compression of digitized sound. IIR filter 304emphasizes recent raw residual magnitudes while less recent raw residualmagnitudes have less emphasis in the filtered residual. At the sametime, IIR filter 304 is particularly simple and executes particularlyefficiently.

Clamped Residuals

Coder 110 does not encode raw residuals from subtracter 112. Instead,coder 110 encodes clamped residuals from subtracter 114. Subtracter 114measures a clamped residual as a difference between the current sourcesample and a clamped predicted sample received from clamp 108. Clamp 108limits the predicted sample produced by predictor 102 and received fromdelay 104 to within a limited range of predicted samples. FIG. 4 isillustrative.

FIG. 4 shows a range 402 of valid sample values that can be representedby n bits. It should be noted that a binary word having n bits canrepresent a limited range of values. Any predicted value outside of thatlimited range, as represented by range 402, cannot possibly be theactual value of the current sample since the current sample isinherently limited to the values within range 402. Clamp 108 preventsconsideration of predicted values outside of range 402 and improvescompression rates as a result.

Assume for this illustrative example that predictor 102 (FIG. 1)produces a predicted sample 404 (FIG. 4) whose value is very near or atthe maximum value that can be represented using n bits. Range 406represents the range of source sample values that can be represented bya residual using only least significant binary portion 508 (FIG. 5C) ofa Golomb-Rice encoded residual. A portion of range 406 (FIG. 4) extendsbeyond the maximum value of range 402. That portion of range 406 iswasted since no valid source sample can have a value outside of range402. This waste is more clearly illustrated by comparison to a clampedpredicted sample 408.

Clamped predicted sample 408 is limited to a maximum value which is lessthan the maximum of range 402 by the non-negative range of leastsignificant binary portion 508 (FIG. 5C), i.e., by the maximum amount bywhich least significant binary portion 508 can increase a predictedsample. Accordingly, range 410 (FIG. 4), which represents the range ofsource sample values that can be represented by a residual using onlyleast significant binary portion 508 (FIG. 5C) of a Golomb-Rice encodedresidual based on clamped predicted sample 408 (FIG. 4), extends up tobut not beyond the maximum value of range 402. If the value of thecurrent source sample is within range 412A, encoding the residual frompredicted sample 404 requires two (2) bits for most significant unaryportion 506 (FIG. 5C) in addition to k bits for least significant binaryportion 504. By comparison, the current source sample within range 412A(FIG. 4) can be encoded from a residual from clamped predicted sample408 using only one (1) bit for most significant unary portion 506 (FIG.5C). Thus, for some source samples, a bit is saved by clamping thepredicted sample.

Such savings are further realized for other source samples, e.g., sourcesamples in ranges 412B-D. In range 412B, predicted sample 404 requiresthree (3) bits for most significant unary portion 506 (FIG. 5C) whileclamped predicted sample 408 (FIG. 4) requires two (2) bits for mostsignificant unary portion 506 (FIG. 5C). In range 412C (FIG. 4),predicted sample 404 requires four (4) bits for most significant unaryportion 506 (FIG. 5C) while clamped predicted sample 408 (FIG. 4)requires three (3) bits for most significant unary portion 506 (FIG.5C). In range 412D (FIG. 4), predicted sample 404 requires five (5) bitsfor most significant unary portion 506 (FIG. 5C) while clamped predictedsample 408 (FIG. 4) requires four (4) bits for most significant unaryportion 506 (FIG. 5C).

Predictor 102 (FIG. 1) can produce a predicted sample, e.g., predictedsample 414 (FIG. 4), which is well beyond range 402. While predictor 102is designed to predict source samples with particular accuracy, suchsample prediction cannot be perfect and anomalous predictions welloutside range 402 are possible. In this illustrative example, at leastfour (4) bits for most significant unary portion 506 (FIG. 5C) arerequired to represent a residual from predicted sample 414 (FIG. 4)which corresponds to a source sample which is necessarily within range402. Clamp 108 (FIG. 1) limits all predicted samples to no more thanclamped predicted sample 408 (FIG. 4).

Clamp 108 (FIG. 1) realizes analogous benefits at the other end of range402 (FIG. 4) by limiting predicted samples to a minimum clampedpredicted sample. Specifically, the minimum clamped predicted sample islimited to a minimum value which is greater than the minimum of range402 by the non-positive range of least significant binary portion 508(FIG. 5C), i.e., by the maximum amount by which least significant binaryportion 504 can decrease a predicted sample. To determine the maximumand minimum clamped predicted samples, clamp 108 (FIG. 1) receives thecode length, k, from code length adapter 106. In this illustrativeembodiment, the maximum and minimum clamped predicted samples are2^(n-1) -1/2(2^(k))-1 and -2^(n-1) +1/2(2^(k)), respectively.

Subtracter 114 receives the current source sample and, from clamp 108,the clamped predicted sample and measures as a difference therebetween aclamped residual. Coder 110 receives the code length from code lengthadapter 106 and the clamped residual from subtracter 114 and encodes theclamped residual as described above with least significant binaryportion 504 (FIG. 5) having a data length defined by the received codelength.

Adaptation of the Adaptation Rate

To compress a digitized signal, e.g., a digitized sound signal, usinglossless compressor 100 (FIG. 1), each of the individual digital samplesof the digitized signal are encoded by lossless compressor 100 in themanner described above. As described above, the aggressiveness withwhich predictor 102 (FIG. 2) adapts to prediction errors in the form ofraw residuals is controlled by adaptation rate 208. Specifically,predictor coefficient adapter 206 (FIG. 11) uses adaptation rate 208 toweight the product of the raw residual and previous source sample 1102(FIG. 11) as stored in delay buffer 202, which represents an error inthe prediction of the current sample attributable to previous sourcesample 1102, prior to adjusting filter coefficient 1104 using theweighted product. It is quite difficult to select an adaptation ratewhich produces good compression rates for an entire digitized signal.For example, digitized music frequently has soft, quiet passages as wellas much more dynamic passages. With such a signal, a lower adaptationrate produces better results for the quiet passages while a higheradaptation rate produces better results for the more dynamic passages.Therefore, adaption rate 208 is periodically adapted.

In encoding an entire signal, e.g., a digitized audio signal such as amusical composition, each of a multitude of individual digital samplesare encoded by lossless compressor 100 (FIG. 1) and transmitted inencoded form to a lossless decompressor 700 (FIG. 7) which decodes theencoded samples to recreate the original digital samples of the entiresignal and which is described more completely below. To periodicallyadapt adaptation rate 208 (FIG. 2), the individual digital samples areencoded in blocks of a predetermined size. In one embodiment, a blockincludes 2048 digital samples. Of course, larger or smaller block sizescan be used to adapt adaptation rate 208 less or more frequently,respectively. In sending each block of samples to lossless decompressor700 (FIG. 7), a block header is included. Accordingly, using smallerblock sizes also increases data transmission overhead. If adaptationrate 208 (FIG. 2) does not change for several blocks, a larger blocksize can be used to reduce data transmission overhead. Conversely, usingsmaller blocks allows lossless compressor 100 (FIG. 1) to adjustadaptation rate 208 (FIG. 2) more frequently to thereby achieve bettercompression rates. If adaptation rate 208 changes frequently and by morethan a minimum increment, a smaller block size can allow adaptation rate208 to adapt more immediately to changes in the character of the digitalsignal and achieve improved compression rates notwithstanding dynamicqualities of the digital signal.

The block header includes data specifying (i) the adaptation rate usedby predictor 102, i.e., adaptation rate 208, (ii) the number of samplesin the block, (iii) the total number of bits of the block, (iv) whetherthe samples of the block are compressed or are in their native,uncompressed form, (v) whether the block includes data specifying a newstate for lossless decompressor 700 (FIG. 7). For each block encoded bylossless compressor 100 (FIG. 1), adaptation rate 208 (FIG. 2) isadapted according to logic flow diagram 600 (FIG. 6).

Processing according to logic flow diagram 600 begins with step 602 inwhich the adaptation rate used in compressing the previous block isselected and stored as adaptation rate 208 (FIG. 2). During a particularperformance of the steps of logic flow diagram 600 (FIG. 6), theadaptation rate used in compressing the previous block is referred to asthe original adaptation rate. Processing transfers to step 604 in whichthe current block is encoded using adaptation rate 208 (FIG. 2) as setin step 602 (FIG. 6).

In step 606, lossless compressor 100 (FIG. 1) increments adaptation rate208 (FIG. 11). It should be noted that, in this illustrative embodiment,adaptation rate 208 represents an exponent since multiplier 1106 effectsmultiplication using left bit-wise shifting by a number of bit-placesrepresented by adaptation rate 208. Accordingly, incrementing adaptationrate 208 in this embodiment effectively doubles the rate at whichpredictor coefficients are adapted in response to raw residuals.Lossless compressor 100 (FIG. 1) encodes the current block usingadaptation rate 208 (FIG. 2) as incremented in step 606 (FIG. 6).Processing transfers from step 606 to test step 610. In test step 610,lossless compressor 100 (FIG. 1) determines whether the most recentencoding of the current block is improved, i.e., more compact, than thepreviously best encoding of the current block. If so, processingtransfers to step 606 in which adaptation rate 208 (FIG. 2) is againincremented and, therethrough, to step 608 (FIG. 6) in which the currentblock is again encoded. Conversely, if the most recent encoding of thecurrent block is not an improvement, processing transfers from test step610 (FIG. 6) to test step 612.

In test step 612, lossless compressor 100 (FIG. 1) determines whetherincrementing adaptation rate 208 (FIG. 2) improved encoding of thecurrent block in relation to encoding of the current block using theoriginal adaptation rate. If so, lossless compressor 100 (FIG. 1) doesnot try decrementing adaptation rate 208 (FIG. 2) from the originaladaptation rate in steps 614-622 as described below. Instead, processingtransfers directly to step 624 in which lossless compressor 100 (FIG. 1)selects the best, i.e., most compact, encoding of the current block andthe corresponding adaptation rate. Conversely, if incrementingadaptation rate 208 (FIG. 2) did not improve encoding of the currentblock in relation to encoding of the current block using the originaladaptation rate, lossless compressor 100 (FIG. 1) attempts improvementof encoding of the current block by decrementing adaptation rate 208(FIG. 2) in steps 614-622 (FIG. 6).

In step 614, lossless compressor 100 (FIG. 1) resets adaptation rate 208(FIG. 2) to the original adaptation rate. Lossless compressor 100 (FIG.1), in step 616 (FIG. 6), encodes the current block using adaptationrate 208 (FIG. 2) as set in step 614 (FIG. 6).

In step 618, lossless compressor 100 (FIG. 1) decrements adaptation rate208 (FIG. 2). Lossless compressor 100 (FIG. 1), in step 620 (FIG. 6),encodes the current block using adaptation rate 208 (FIG. 2) asdecremented in step 618 (FIG. 6). Again, it should be noted that, inthis illustrative embodiment, adaptation rate 208 represents an exponentand multiplier 1106 effects multiplication using left bit-wise shiftingby a number of bit-places represented by adaptation rate 208.Accordingly, decrementing adaptation rate 208 in this embodimenteffectively halves the rate at which predictor coefficients are adaptedin response to raw residuals. Processing transfers from step 620 to teststep 622. In test step 622, lossless compressor 100 (FIG. 1) determineswhether the most recently encoding of the current block is improved,i.e., more compact, than the previously best encoding of the currentblock. If so, processing transfers to step 618 (FIG. 6) in whichadaptation rate 208 (FIG. 2) is again decremented and, therethrough, tostep 620 (FIG. 6) in which the current block is again encoded.Conversely, if the most recent encoding of the current block is not animprovement, processing transfers from test step 622 (FIG. 6) to teststep 624. As described above, lossless compressor 100 (FIG. 1) selectsthe best, i.e., most compact, encoding of the current block and thecorresponding adaptation rate in step 624 (FIG. 6).

Thus, in accordance with logic flow diagram 600, lossless compressor 100(FIG. 1) periodically adjusts adaptation rate 208 (FIG. 2) to optimizecompression of individual blocks of the digital signal. As describedabove, incremental changes in adaptation rate 208 effectively double orhalf the rate at which predictor coefficients 204 adapt in accordancewith a raw residual. Accordingly, an optimum adaptation rate is achievedwith few adjustments to adaptation rate 208. Since each adjustment ofadaptation rate 208 requires another iterative lossless compression ofsamples of a particular block, reducing the number of adjustments toadaptation rate 208 significantly reduces the amount of processingrequired to optimize adaptation rate 208. In addition, since smallchanges in adaptation rate 208 result in large changes in the adaptationof predictor coefficients 204, adaptation rate 208 changes only inresponse to significant changes in the signal characteristics of thedigitized audio signal processed by lossless compressor 100 (FIG. 1). Inother words, adaptation rate 208 is relatively stable and larger blocksizes can be used to reduce encoded signal overhead without incurring apenalty in compression rate.

Decompression

A lossless decompressor 700 (FIG. 7) receives encoded sample residualsfrom lossless compressor 100 and reconstructs digital samples of thedigital signal therefrom. Such encoded sample residuals from losslesscompressor 100 are typically stored in a computer memory medium forarchival purposes and/or transmitted over computer communications mediaprior to receipt by lossless decompressor 700.

Lossless decompressor 700 includes a decoder 702 which receives theencoded sample residuals and decodes the encoded sample residual toproduce a residual signal. Decoder 702 applies the inverse of theencoding applied by coder 110 as described above such that the decodedresidual signal is equivalent to the clamped residual described above.Specifically, decoder 702 translates a unary representation of mostsignificant portion 506 (FIG. 5C) to a binary representation which isthen combined with binary least significant portion 508 to thereby formdata word 502B. Using the illustrative example given above in which k isequal to five (5), decoder 702 (FIG. 7) interprets the first five (5)bits, e.g., "1 1101," as a binary representation of least significantbinary portion 508 (FIG. 5C). Following the first five (5) bits is aunary representation of most significant unary portion 506. As describedabove, coder 110 (FIG. 1) represents this as two (2) "1" bits delimitedby a "0" bits. Accordingly, decoder 702 (FIG. 7) decodes mostsignificant unary portion 506 (FIG. 5C) as "0000 0000 010." Combiningmost significant unary portion 506 with least significant binary portion508, decoder 702 (FIG. 7) reconstructs the residual signal, "0000 00000101 1101."

In addition, decoder 702 restores signed data word 502A (FIG. 5A) fromunsigned data word 502B (FIGS. 5B-C). Specifically, decoder 702 (FIG. 7)retrieves and saves sign bit 504 (FIG. 5B) and bit-shifts data word 502Bto the right by one bit position. If sign bit 504 indicates a negativevalue, decoder 702 (FIG. 7) negates the bit-shifted value to form dataword 502A (FIG. 5A). Conversely, if sign bit 504 indicates anon-negative value, decoder 702 (FIG. 7) stores the bit-shifted valuewithout negation as data word 502A (FIG. 5A).

Decoder 702 uses an adapted code length received from a code lengthadapter 710, which adjusts the Golomb-Rice encoding parameter k inresponse to a reconstructed raw residual signal in the manner that codelength adapter 106 (FIG. 1) adjusts the Golomb-Rice encoding parameter kin response to the raw residual as described above. The reconstructedresidual signal produced by decoder 702 corresponds to the clampedresidual produced by subtracter 114 (FIG. 1) and therefore can differfrom the raw residual signal used by code length adapter 106 (FIG. 1) todetermine the code length. Accordingly, code length adapter 710 (FIG. 7)determines the code length as represented by encoding parameter k from areconstructed raw residual signal produced at node 716 by a subtracter714 in a manner described more completely below.

Decoder 702 (FIG. 7) provides the decoded residual signal to an adder704 which combines the decoded residual signal with a predicted samplesignal. Since the residual encoded by coder 110 (FIG. 1) was clamped byclamp 108, the reconstructed residual signal produced by decoder 702(FIG. 7) should be similarly combined with a clamped predicted samplesignal. A predictor 706 produces the predicted sample signal from areconstructed sample signal from adder 704 and stores the reconstructedsample signal in a delay 708. Predictor 706 uses the same logic used bypredictor 102 (FIG. 1) to make identical predictions with respect tonext samples as are made by predictor 102 in forming encoded sampleresiduals decompressed by lossless decompressor 700 (FIG. 7). Predictor706 receives the reconstructed residual signal from decoder 702 andadapts in the manner that predictor 102 (FIG. 2) adapts in response tothe raw residual as described above. To do so, predictor 706 receivesthe reconstructed raw residual from node 716. In addition, losslessdecompressor 700 periodically receives header information from losslesscompressor 100 which can include a new adaptation rate analogous toadaptation rate 208 (FIG. 2). Thus, predictor 706 (FIG. 7) predictssample signals in a manner which is directly analogous to, andconsistent with, predictor 102 (FIG. 1) to ensure accuratereconstruction of the encoded and compressed samples.

The difference between the previously predicted sample signal stored indelay 708 and a currently reconstructed sample is measured by asubtracter 714 and produced at node 716. As described above, thereconstructed raw residual signal from node 716 is used by predictor 706to adapt in the manner described above with respect to predictor 102 andis used by code length adapter 710 (FIG. 7) to adapt the code length inthe manner described above with respect to code length adapter 106 (FIG.1).

The predicted sample signal stored in delay 708 is received by a clamp712 which clamps the predicted sample signal in the manner describedabove with respect to clamp 108 (FIG. 1) to produce a clamped predictedsample at 718 (FIG. 7). Accordingly, the clamped predicted sample at 718is equivalent to the clamped predicted sample received by subtracter 114(FIG. 1) immediately prior to encoding by coder 110. Accordingly,combination of the clamped predicted sample signal at 718 with thedecoded residual signal at adder 704 faithfully reconstructs the samplecompressed by lossless compressor 100 (FIG. 1).

Improved Prediction in Multi-Channel Audio Signals

As described above, better compression rates are realized by losslesscompressor 100 when predictor 102 more accurately predicts the nextsource sample. A source sample can be more accurately predicted using,in addition to previous samples, corresponding samples of a companiondigital signal. For example, in a digitized audio signal which is stereoand therefore divided into left and right channels of correspondingsamples, there is a high degree of correlation between correspondingsamples of the left and right channels. Accordingly, a current sample ofone of the companion channels is used to predict a corresponding currentsample of another of the companion channels to more accurately predictthe corresponding current sample. FIG. 8 is illustrative.

FIG. 8 shows predictors 802L and 802R for left and right channels,respectively, of a digitized stereo audio signal. The left and rightchannels are companion channels in that the content of the left andright channels are each components of a single, synchronizedpresentation. Specifically, the left and right channels are intended forsynchronized, simultaneous playback as a single audio presentation. As aresult, a given sample of the left channel audio signal is a relativelygood predictor of a corresponding sample of the right channel audiosignal.

Predictor 802L receives a current sample of the left channel audiosignal and produces a predicted next sample of the left channel audiosignal in a manner which is analogous to that described above withrespect to predictor 102 (FIG. 1). In addition to the current sample ofthe left channel audio signal, predictor 802L (FIG. 8) also uses thecurrent sample of the right channel audio signal and previously receivedsamples of the left and right channel audio signals. For example, inthis illustrative embodiment, predictor 802L applies a least mean squareadaptive linear filter to these current and previously received samplesof the left and right channel audio signals to predict the next sampleof the left channel audio signal. Predictor 802L is described below ingreater detail in the context of FIG. 9B.

Predictor 802R (FIG. 8) receives a current sample from the right channelaudio signal and a look ahead sample of the left audio channel. The lookahead sample of the left audio channel corresponds to the next sample ofthe right channel to be subsequently processed by predictor 802R. Aftera single delay, e.g., a delay provided by left channel look ahead buffer922 (FIG. 9A), the look ahead sample of the left audio channel becomes acurrent sample of the left audio channel. Predictor 802R (FIG. 8) usesthe current and previously received samples of the left and rightchannel audio signals and the look ahead sample of the left channelaudio signal in a linear, least mean square, adaptive filter, in oneembodiment, to predict the next sample of the right channel audiosignal. Thus, the look ahead sample of the left channel audio signal isused to predict a corresponding next sample of the right channel audiosignal. Predictor 802R is shown in greater detail in FIG. 9A.

Predictor 802R includes delay buffer 902R, predictor coefficients 904R,predictor coefficient adapter 906R, and adaptation rate 908R which aredirectly analogous to delay buffer 202 (FIG. 2), predictor coefficients204, predictor coefficient adapter 206, and adaptation rate 208,respectively, of predictor 102. Predictor 802R (FIG. 9) weights a leftchannel look ahead sample stored in left channel look ahead buffer 922using a left channel coefficient 920. Predictor 802R combines theweighted left channel look ahead sample with current and previous leftand right samples stored in delay buffer 902R as weighted according toadapted predictor coefficients 904R to form a right channel predictedsample. Interleaver 924R interleaves left and right samples of the leftand right channels, respectively, into the single delay buffer 902R. Inone embodiment, left channel coefficient 920 is adaptive usingadaptation rate 908 or, alternatively, an independent adaptation rate,in the manner described above with respect to adaptation rate 208 (FIG.11).

Predictor 802L (FIG. 9B) includes delay buffer 902L, predictorcoefficients 904L, predictor coefficient adapter 906L, adaptation rate908L, and interleaver 924L which are directly analogous to delay buffer902R (FIG. 9A), predictor coefficients 904R, predictor coefficientadapter 906R, adaptation rate 908R, and interleaver 924R, respectively,of predictor 802R. Predictor 802L (FIG. 9B) combines current andprevious left and right samples stored in delay buffer 902L as weightedaccording to adapted predictor coefficients 904L to form a left channelpredicted sample. Interleaver 924L interleaves left and right samples ofthe left and right channels, respectively, into the single delay buffer902L.

Since the samples of the left and right channels are relatively closelyrelated as described above, the right channel predicted sample producedby predictor 80R (FIG. 9A) benefits from a look ahead sample of the leftaudio channel and more accurately predicts the next right channelsample. Accordingly, any residual between the right channel predictedsample and the next right channel sample is reduced. In addition, fewerbits can be used to represent such a residual and better compressionrates are realized.

FIG. 10 shows a portion of a stereo decompressor 1000 in accordance withthe present invention. Stereo decompressor 1000 includes a left channel,which includes a predictor 1002L, a delay 1004L, and an adder 1006L, anda right channel, which includes a predictor 1002R, a delay 1004R, and anadder 1006R. Predictor 1002L receives a previously reconstructed leftchannel sample from delay 1004L and a previously reconstructed rightchannel sample from delay 1004R and predicts a current left channelsample in generally the manner described above with respect to predictor802L (FIGS. 8 and 9B). Adder 1006L (FIG. 10) combines the predictedcurrent left channel sample with a decoded left channel residual toreconstruct a current left channel sample.

Predictor 1002R receive a previously reconstructed right channel samplefrom delay 1004R and receives the current reconstructed left channelsample from adder 1006L. Predictor 1002R uses the previouslyreconstructed right channel sample and the current reconstructed leftchannel sample to predict a current right channel sample in the mannerdescribed above with respect to predictor 802R (FIGS. 8 and 9A). Sincepredictor 1002R (FIG. 10) requires the current reconstructed leftchannel sample produced from adder 1006L, execution of predictor 1002Ris subsequent to predictor 1002L. Adder 1006R combines the predictedcurrent right channel sample with a decoded right channel residual toreconstruct a current right channel sample.

Thus, in a digitized audio signal with multiple channels, an actualsample from one channel is used to more accurately predict acorresponding sample of another channel. This concept can be used indigitized audio signals with more than two channels, e.g., quadraphonicaudio signals. For example, an actual sample from the left front channelis used in the prediction of corresponding samples of the right front,right rear, and left rear channels. In addition, an actual sample of theright front channel is used in the prediction of corresponding samplesof the right rear and left rear channels. Furthermore, an actual sampleof the left rear channel is used in the prediction of a correspondingsample of the right rear channel. In other words, (i) predicting asample of the right front channel has the benefit of an actualcorresponding sample of the left front channel, (ii) predicting a sampleof the left rear channel has the benefit of actual corresponding samplesof the left front and right front channels, and (iii) predicting asample of the right rear channel has the benefit of actual correspondingsamples of the left front, right front, and left rear channels.Accordingly, the corresponding samples of a multiple channel digitizedaudio signal are predicted with significantly improved accuracy and arecompressed with a commensurately improved compression rate.

Inclusion of Lossless Compressor 100 in a Computer System

In general, lossless compressor 100 (FIG. 1) compresses signals such asdigitized audio signals for such purposes as archival storage or fortransmission through a computer network. Lossless compressor 100executes within a computer system 1200 (FIG. 12) as described morecompletely below. In addition, lossless decompressor 700 (FIG. 7) alsoexecutes within computer system 1200 (FIG. 12) to restore the compressedsignal to its original form. As shown in FIG. 12, lossless compressor100 and lossless decompressor 700 both execute within the same computersystem. Such would be appropriate if lossless compressor 100 is used tostore the signal, e.g., source signal 1240, in a compressed form forarchival purposes. However, lossless decompressor 700 can also executein a remote computer system (not shown) which is coupled to computersystem 1200 through a computer network 1280 such that losslessdecompressor 700 can restore source signal 1240 as reconstructed sourcesignal 1240B from encoded signal 1250 after receiving encoded signal1250 through computer network 1280. Lossless compressor 100 and losslessdecompressor 700 execute sufficiently efficiently that such compressionand decompression can be accomplished by lossless compressor 100 andlossless decompressor 700, respectively, during real-time streaming ofsource signal 1240, in the form of a compressed encoded signal 1250,through computer network 1280.

Computer system 1200 includes a processor 1202 and memory 1204 which iscoupled to processor 1202 through an interconnect 1206. Interconnect1006 can be generally any interconnect mechanism for computer systemcomponents and can be, e.g., a bus, a crossbar, a mesh, a torus, or ahypercube. Processor 1202 fetches from memory 1204 computer instructionsand executes the fetched computer instructions. In addition, processor1202 can fetch computer instructions through computer network 1280through network access circuitry 1260 such as a modem or ethernetnetwork access circuitry. Processor 1202 also reads data from and writesdata to memory 1204 and sends data and control signals throughinterconnect 1206 to one or more computer display devices 1220 andreceives data and control signals through interconnect 1206 from one ormore computer user input devices 1230 in accordance with fetched andexecuted computer instructions.

Memory 1204 can include any type of computer memory and can include,without limitation, randomly accessible memory (RAM), read-only memory(ROM), and storage devices which include storage media such as magneticand/or optical disks. Memory 1204 includes lossless compressor 100 andlossless decompressor 700 which are each all or part of one or morecomputer processes which in turn execute within processor 1202 frommemory 1204. A computer process is generally a collection of computerinstructions and data which collectively define a task performed bycomputer system 1200. Memory 1204 also includes, in this illustrativeembodiment, source signal 1240, encoded signal 1250, and reconstructedsource signal 1240B.

Each of computer display devices 1220 can be any type of computerdisplay device including without limitation a printer, a cathode raytube (CRT), a light-emitting diode (LED) display, or a liquid crystaldisplay (LCD). Each of computer display devices 1220 receives fromprocessor 1202 control signals and data and, in response to such controlsignals, displays the received data. Computer display devices 1220, andthe control thereof by processor 1202, are conventional.

Each of user input devices 1230 can be any type of user input deviceincluding, without limitation, a keyboard, a numeric keypad, or apointing device such as an electronic mouse, trackball, lightpen,touch-sensitive pad, digitizing tablet, thumb wheels, or joystick. Eachof user input devices generates signals in response to physicalmanipulation by a user and transmits those signals through interconnect1206 to processor 1202.

Computer system 1200 also includes signal acquisition circuitry 1270which can be, for example, a microphone and sound capture circuitry.Sound captured by signal acquisition circuitry 1270 are stored in abuffer in memory 1204 as source signal 1240. Alternatively, sounds canbe captured separately, i.e., by another computer system, and stored inmemory 1204 as source signal 1240 for lossless compression and deliveryto a remote computer system through computer network 1280 upon request.In addition, source signal 1240 can be generated by processing ofprocessor 1202 or by another computer in response to computerinstructions specified by a user of computer system 1200 throughphysical manipulation of one or more of user input devices 1230 andstored in memory 1204.

As described above, lossless compressor 100 executes within processor1202 from memory 1204. Specifically, processor 1202 fetches computerinstructions from lossless compressor 100 and executes those computerinstructions. Processor 1202, in executing lossless compressor 100,reads samples from source signal 1240, processes and encodes thosesamples in the manner described above, and stores the encoded residualsand packet headers in encoded signal 1250 or can transmit the encodedresiduals and packet headers immediately through computer network 1280to a remote computer system (not shown).

Lossless decompressor 700 is all or part of a computer process executingwithin processor 1202 from memory 1204. Lossless decompressor 700receives encoded residuals and packet headers from encoded signal 1250and reconstructs samples of source signal 1240 and stores thereconstructed samples in reconstructed source signal 1240B.Reconstructed source signal 1240B is equivalent to source signal 1240and can be used in any manner in which source signal 1240 can be used.For example, if source signal 1240 is an audio signal, reconstructedsource signal 1240B is an equivalent audio signal and can be reproducedto present the sound represented by both source signal 1240 andreconstructed source signal 1240B.

The above description is illustrative only and is not limiting. Thepresent invention is limited only by the claims which follow.

What is claimed is:
 1. A method for compressing a signal which includesone or more samples, the method comprising:compressing the samples ofthe signal using an adaptive predictor which predicts samples in anadaptive manner using an adaptation rate to form a first compressedsignal; measuring a performance characteristic of the first compressedsignal; adjusting the adaptation rate; compressing the samples of thesignal using the adaptive predictor in accordance with the adaptationrate as adjusted to form a second compressed signal; measuring aperformance characteristic of the second compressed signal; selecting aselected one of the first and second compressed signals according to theperformance characteristics of the respective compressed signals; andincorporating the selected compressed signal into a resulting compressedsignal.
 2. The method of claim 1 wherein the performance characteristicis the amount of data required to represent the compressed signal. 3.The method of claim 1 wherein the step of adjusting comprisesincrementing the adaptation rate.
 4. The method of claim 1 wherein thestep of adjusting comprises decrementing the adaptation rate.
 5. Themethod of claim 1 wherein the adaptation rate represents an exponent ofan adaptation factor such that unitary adjustments in the adaptationrate change the rate at which the adaptive predictor adaptsexponentially.
 6. The method of claim 1 wherein the step of adjustingdoubles the rate at which the adaptive predictor adapts.
 7. The methodof claim 1 wherein the step of adjusting halves the rate at which theadaptive predictor adapts.
 8. A computer readable medium useful inassociation with a computer which includes a processor and a memory, thecomputer readable medium including computer instructions which areconfigured to cause the computer to compress a signal which includes oneor more samples by performing the steps of:compressing the samples ofthe signal using an adaptive predictor which predicts samples in anadaptive manner using an adaptation rate to form a first compressedsignal; measuring a performance characteristic of the first compressedsignal; adjusting the adaptation rate; compressing the samples of thesignal using the adaptive predictor in accordance with the adaptationrate as adjusted to form a second compressed signal; measuring aperformance characteristic of the second compressed signal; selecting aselected one of the first and second compressed signals according to theperformance characteristics of the respective compressed signals; andincorporating the selected compressed signal into a resulting compressedsignal.
 9. The computer readable medium of claim 8 wherein theperformance characteristic is the amount of data required to representthe compressed signal.
 10. The computer readable medium of claim 8wherein the step of adjusting comprises incrementing the adaptationrate.
 11. The computer readable medium of claim 8 wherein the step ofadjusting comprises decrementing the adaptation rate.
 12. The computerreadable medium of claim 8 wherein the adaptation rate represents anexponent of an adaptation factor such that unitary adjustments in theadaptation rate change the rate at which the adaptive predictor adaptsexponentially.
 13. The computer readable medium of claim 8 wherein thestep of adjusting doubles the rate at which the adaptive predictoradapts.
 14. The computer readable medium of claim 8 wherein the step ofadjusting halves the rate at which the adaptive predictor adapts.
 15. Acomputer system comprising:a processor; a memory operatively coupled tothe processor; and a compressor which executes in the processor from thememory and which, when executed by the processor, causes the computer tocompress a signal which includes one or more samples by performing thesteps of:compressing the samples of the signal using an adaptivepredictor which predicts samples in an adaptive manner using anadaptation rate to form a first compressed signal; measuring aperformance characteristic of the first compressed signal; adjusting theadaptation rate; compressing the samples of the signal using theadaptive predictor in accordance with the adaptation rate as adjusted toform a second compressed signal; measuring a performance characteristicof the second compressed signal, selecting a selected one of the firstand second compressed signals according to the performancecharacteristics of the respective compressed signals; and incorporatingthe selected compressed signal into a resulting compressed signal. 16.The computer system of claim 15 wherein the performance characteristicis the amount of data required to represent the compressed signal. 17.The computer system of claim 15 wherein the step of adjusting comprisesincrementing the adaptation rate.
 18. The computer system of claim 15wherein the step of adjusting comprises decrementing the adaptationrate.
 19. The computer system of claim 15 wherein the adaptation raterepresents an exponent of an adaptation factor such that unitaryadjustments in the adaptation rate change the rate at which the adaptivepredictor adapts exponentially.
 20. The computer system of claim 15wherein the step of adjusting doubles the rate at which the adaptivepredictor adapts.
 21. The computer system of claim 15 wherein the stepof adjusting halves the rate at which the adaptive predictor adapts.